Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	21,224
2	Louisiana	20,799
3	Arizona	20,433
4	New Jersey	20,178
5	Florida	17,219
6	Rhode Island	16,978
7	Massachusetts	16,544
8	District of Columbia	16,191
9	Mississippi	15,296
10	Alabama	14,349
11	South Carolina	14,243
12	Delaware	14,116
13	Connecticut	13,490
14	Maryland	13,250
15	Illinois	13,063
16	Georgia	12,796
17	Iowa	12,589
18	Texas	12,263
19	Nevada	12,208
20	Nebraska	11,988
21	Tennessee	11,718
22	Arkansas	11,483
23	Utah	10,944
24	California	10,385
25	North Carolina	9,835
26	Virginia	9,298
27	South Dakota	9,064
28	Idaho	8,896
29	Indiana	8,814
30	Minnesota	8,422
31	Pennsylvania	8,394
32	New Mexico	8,354
33	Michigan	8,329
34	Wisconsin	8,225
35	Kansas	8,102
36	Colorado	7,146
37	North Dakota	6,838
38	Washington	6,664
39	Oklahoma	6,653
40	Ohio	6,605
41	Missouri	6,068
42	Kentucky	5,514
43	New Hampshire	4,605
44	Puerto Rico	4,051
45	Wyoming	3,866
46	Oregon	3,617
47	Alaska	3,448
48	West Virginia	2,900
49	Maine	2,769
50	Montana	2,564
51	Vermont	2,189
52	Hawaii	985

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Louisiana	573
2	Florida	500
3	Mississippi	411
4	South Carolina	370
5	Alabama	348
6	Arizona	338
7	Nevada	324
8	Texas	293
9	Idaho	273
10	Tennessee	268
11	Georgia	236
12	Arkansas	234
13	California	229
14	Utah	172
15	Alaska	166
16	North Carolina	158
17	Wisconsin	158
18	Kansas	156
19	Missouri	156
20	Puerto Rico	155
21	Iowa	148
22	Kentucky	140
23	North Dakota	131
24	Maryland	128
25	Washington	125
26	New Mexico	124
27	Minnesota	117
28	Virginia	117
29	Indiana	113
30	Nebraska	111
31	District of Columbia	110
32	Delaware	108
33	Oklahoma	106
34	Ohio	96
35	Illinois	91
36	Montana	84
37	Oregon	81
38	Colorado	77
39	Wyoming	74
40	Pennsylvania	69
41	Michigan	60
42	Rhode Island	60
43	South Dakota	59
44	West Virginia	51
45	Massachusetts	38
46	New York	32
47	Maine	19
48	Connecticut	18
49	New Hampshire	18
50	New Jersey	16
51	Hawaii	15
52	Vermont	14

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,771
2	New York	1,656
3	Connecticut	1,235
4	Massachusetts	1,225
5	Rhode Island	940
6	District of Columbia	821
7	Louisiana	776
8	Michigan	639
9	Illinois	593
10	Maryland	562
11	Pennsylvania	554
12	Delaware	539
13	Mississippi	466
14	Indiana	422
15	Arizona	400
16	Colorado	306
17	Georgia	300
18	New Hampshire	294
19	Minnesota	281
20	New Mexico	280
21	Ohio	275
22	Alabama	265
23	Iowa	256
24	Florida	242
25	Virginia	239
26	South Carolina	237
27	Nevada	220
28	Washington	201
29	California	199
30	Missouri	191
31	North Carolina	162
32	Nebraska	161
33	Kentucky	155
34	Wisconsin	149
35	Texas	148
36	South Dakota	133
37	North Dakota	128
38	Tennessee	126
39	Arkansas	123
40	Oklahoma	116
41	Kansas	109
42	Vermont	89
43	Maine	87
44	Utah	79
45	Idaho	71
46	Oregon	64
47	Puerto Rico	56
48	West Virginia	56
49	Wyoming	43
50	Montana	37
51	Alaska	21
52	Hawaii	16

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	8
2	Louisiana	7
3	South Carolina	5
4	Florida	4
5	Mississippi	4
6	Nevada	3
7	New Mexico	3
8	Texas	3
9	Georgia	2
10	Ohio	2
11	Alabama	1
12	Arkansas	1
13	California	1
14	Idaho	1
15	Indiana	1
16	Iowa	1
17	Maryland	1
18	Massachusetts	1
19	New Jersey	1
20	North Carolina	1
21	North Dakota	1
22	Oregon	1
23	Rhode Island	1
24	Tennessee	1
25	Utah	1
26	Alaska	0
27	Colorado	0
28	Connecticut	0
29	Delaware	0
30	District of Columbia	0
31	Hawaii	0
32	Illinois	0
33	Kansas	0
34	Kentucky	0
35	Maine	0
36	Michigan	0
37	Minnesota	0
38	Missouri	0
39	Montana	0
40	Nebraska	0
41	New Hampshire	0
42	New York	0
43	Oklahoma	0
44	Pennsylvania	0
45	Puerto Rico	0
46	South Dakota	0
47	Vermont	0
48	Virginia	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	137,097	1	99
Lake	Tennessee	100,057	2	99
Lee	Arkansas	97,776	3	99
Dakota	Nebraska	93,528	4	99
Buena Vista	Iowa	89,602	5	99
Davidson	Tennessee	26,041	115	96
Richland	South Carolina	15,413	371	88
Orange	California	9,754	801	74
York	South Carolina	8,947	894	71
Pierce	Washington	4,837	1550	50

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	2,886	5	99
Richland	South Carolina	269	656	79
Davidson	Tennessee	248	703	77
Orange	California	162	954	69
Pierce	Washington	134	1082	65
York	South Carolina	57	1635	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.